



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__magic_name__  	=    logging.get_logger(__name__)

__magic_name__  	=    {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def    _lowerCAmelCase  (	A__:      Optional[Any]						,		A__:      Tuple						,		A__:      Optional[int]						,		A__:      Optional[Any]						,		A__:      str						):


    '''simple docstring'''


    for attribute in key.split('''.'''						):
        UpperCAmelCase      =       getattr(_lowerCamelCase						,		_lowerCamelCase						)

    if weight_type is not None:
        UpperCAmelCase      =       getattr(_lowerCamelCase						,		_lowerCamelCase						).shape
    else:
        UpperCAmelCase      =       hf_pointer.shape

    assert hf_shape == value.shape, (
        F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
        F""" {value.shape} for {full_name}"""
    )

    if weight_type == "weight":
        UpperCAmelCase      =       value
    elif weight_type == "weight_g":
        UpperCAmelCase      =       value
    elif weight_type == "weight_v":
        UpperCAmelCase      =       value
    elif weight_type == "bias":
        UpperCAmelCase      =       value
    else:
        UpperCAmelCase      =       value

    logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)




def    _lowerCAmelCase  (	A__:      Tuple						,		A__:      List[str]						,		A__:      str						):


    '''simple docstring'''


    UpperCAmelCase      =       []
    UpperCAmelCase      =       fairseq_model.state_dict()

    UpperCAmelCase      =       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase      =       False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		hf_model.config.feat_extract_norm == '''group'''						,		)
            UpperCAmelCase      =       True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase      =       '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
                    UpperCAmelCase      =       True
                    if "*" in mapped_key:
                        UpperCAmelCase      =       name.split(_lowerCamelCase						)[0].split('''.'''						)[-2]
                        UpperCAmelCase      =       mapped_key.replace('''*'''						,		_lowerCamelCase						)
                    if "weight_g" in name:
                        UpperCAmelCase      =       '''weight_g'''
                    elif "weight_v" in name:
                        UpperCAmelCase      =       '''weight_v'''
                    elif "weight" in name:
                        UpperCAmelCase      =       '''weight'''
                    elif "bias" in name:
                        UpperCAmelCase      =       '''bias'''
                    else:
                        UpperCAmelCase      =       None
                    set_recursively(_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						)
                continue
        if not is_used:
            unused_weights.append(_lowerCamelCase						)

    logger.warning(F"""Unused weights: {unused_weights}"""						)




def    _lowerCAmelCase  (	A__:      Union[str, Any]						,		A__:      Tuple						,		A__:      Any						,		A__:      List[Any]						,		A__:      List[Any]						):


    '''simple docstring'''


    UpperCAmelCase      =       full_name.split('''conv_layers.'''						)[-1]
    UpperCAmelCase      =       name.split('''.'''						)
    UpperCAmelCase      =       int(items[0]						)
    UpperCAmelCase      =       int(items[1]						)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
            )
            UpperCAmelCase      =       value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
            )
            UpperCAmelCase      =       value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                " found."
            )
            UpperCAmelCase      =       value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            UpperCAmelCase      =       value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
    else:
        unused_weights.append(_lowerCamelCase						)




def    _lowerCAmelCase  (	A__:      List[str]						,		A__:      int						):


    '''simple docstring'''


    UpperCAmelCase      =       SEWConfig()
    if is_finetuned:
        UpperCAmelCase      =       model.wav_encoder.wav_model.cfg
    else:
        UpperCAmelCase      =       model.cfg

    UpperCAmelCase      =       fs_config.conv_bias
    UpperCAmelCase      =       eval(fs_config.conv_feature_layers						)
    UpperCAmelCase      =       [x[0] for x in conv_layers]
    UpperCAmelCase      =       [x[1] for x in conv_layers]
    UpperCAmelCase      =       [x[2] for x in conv_layers]
    UpperCAmelCase      =       '''gelu'''
    UpperCAmelCase      =       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    UpperCAmelCase      =       0.0
    UpperCAmelCase      =       fs_config.activation_fn.name
    UpperCAmelCase      =       fs_config.encoder_embed_dim
    UpperCAmelCase      =       0.02
    UpperCAmelCase      =       fs_config.encoder_ffn_embed_dim
    UpperCAmelCase      =       1E-5
    UpperCAmelCase      =       fs_config.encoder_layerdrop
    UpperCAmelCase      =       fs_config.encoder_attention_heads
    UpperCAmelCase      =       fs_config.conv_pos_groups
    UpperCAmelCase      =       fs_config.conv_pos
    UpperCAmelCase      =       len(_lowerCamelCase						)
    UpperCAmelCase      =       fs_config.encoder_layers
    UpperCAmelCase      =       fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        UpperCAmelCase      =       model.cfg
        UpperCAmelCase      =       fs_config.final_dropout
        UpperCAmelCase      =       fs_config.layerdrop
    UpperCAmelCase      =       fs_config.activation_dropout
    UpperCAmelCase      =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    UpperCAmelCase      =       fs_config.attention_dropout
    UpperCAmelCase      =       fs_config.dropout_input
    UpperCAmelCase      =       fs_config.dropout
    UpperCAmelCase      =       fs_config.mask_channel_length
    UpperCAmelCase      =       fs_config.mask_channel_prob
    UpperCAmelCase      =       fs_config.mask_length
    UpperCAmelCase      =       fs_config.mask_prob

    UpperCAmelCase      =       '''Wav2Vec2FeatureExtractor'''
    UpperCAmelCase      =       '''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def    _lowerCAmelCase  (	A__:      Optional[Any]						,		A__:      Dict						,		A__:      str=None						,		A__:      Optional[int]=None						,		A__:      Optional[int]=True						):


    '''simple docstring'''


    if is_finetuned:
        UpperCAmelCase  ,     UpperCAmelCase  ,     UpperCAmelCase      =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]						,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
    else:
        UpperCAmelCase  ,     UpperCAmelCase  ,     UpperCAmelCase      =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

    if config_path is not None:
        UpperCAmelCase      =       SEWConfig.from_pretrained(_lowerCamelCase						)
    else:
        UpperCAmelCase      =       convert_config(model[0]						,		_lowerCamelCase						)
    UpperCAmelCase      =       model[0].eval()

    UpperCAmelCase      =       True if config.feat_extract_norm == '''layer''' else False
    UpperCAmelCase      =       WavaVecaFeatureExtractor(
        feature_size=1						,		sampling_rate=1_6000						,		padding_value=0						,		do_normalize=_lowerCamelCase						,		return_attention_mask=_lowerCamelCase						,		)

    if is_finetuned:
        if dict_path:
            UpperCAmelCase      =       Dictionary.load(_lowerCamelCase						)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCAmelCase      =       target_dict.pad_index
            UpperCAmelCase      =       target_dict.bos_index
            UpperCAmelCase      =       target_dict.pad_index
            UpperCAmelCase      =       target_dict.bos_index
            UpperCAmelCase      =       target_dict.eos_index
            UpperCAmelCase      =       len(target_dict.symbols						)
            UpperCAmelCase      =       os.path.join(_lowerCamelCase						,		'''vocab.json'''						)
            if not os.path.isdir(_lowerCamelCase						):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCamelCase						)						)
                return
            os.makedirs(_lowerCamelCase						,		exist_ok=_lowerCamelCase						)
            with open(_lowerCamelCase						,		'''w'''						,		encoding='''utf-8'''						) as vocab_handle:
                json.dump(target_dict.indices						,		_lowerCamelCase						)
            UpperCAmelCase      =       WavaVecaCTCTokenizer(
                _lowerCamelCase						,		unk_token=target_dict.unk_word						,		pad_token=target_dict.pad_word						,		bos_token=target_dict.bos_word						,		eos_token=target_dict.eos_word						,		word_delimiter_token='''|'''						,		do_lower_case=_lowerCamelCase						,		)
            UpperCAmelCase      =       WavaVecaProcessor(feature_extractor=_lowerCamelCase						,		tokenizer=_lowerCamelCase						)
            processor.save_pretrained(_lowerCamelCase						)

        UpperCAmelCase      =       SEWForCTC(_lowerCamelCase						)
    else:
        UpperCAmelCase      =       SEWModel(_lowerCamelCase						)
        feature_extractor.save_pretrained(_lowerCamelCase						)

    recursively_load_weights(_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						)

    hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
    __magic_name__  	=    argparse.ArgumentParser()
    parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
    parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
    parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
    parser.add_argument(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    __magic_name__  	=    parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )






"""simple docstring"""






import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 			lowercase					( lowercase__		):
			lowercase        =     '''segformer'''






			def __init__(self  :	str      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=3      ,SCREAMING_SNAKE_CASE_  :	List[str]=4      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[2, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[str]=[8, 4, 2, 1]      ,SCREAMING_SNAKE_CASE_  :	int=[32, 64, 160, 256]      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=[7, 3, 3, 3]      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=[4, 2, 2, 2]      ,SCREAMING_SNAKE_CASE_  :	List[Any]=[1, 2, 5, 8]      ,SCREAMING_SNAKE_CASE_  :	Dict=[4, 4, 4, 4]      ,SCREAMING_SNAKE_CASE_  :	Optional[int]="gelu"      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=0.0      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.02      ,SCREAMING_SNAKE_CASE_  :	str=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=1e-6      ,SCREAMING_SNAKE_CASE_  :	List[str]=256      ,SCREAMING_SNAKE_CASE_  :	List[str]=255      ,**SCREAMING_SNAKE_CASE_  :	Tuple      ,)       ->	int:





				"""simple docstring"""


				super().__init__(**SCREAMING_SNAKE_CASE_			)

				if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
					warnings.warn(
					    '''Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'''
					    ''' removed, as the behaviour will default to that of reshape_last_stage = True.'''      ,SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  num_channels
				lowerCAmelCase												=  num_encoder_blocks
				lowerCAmelCase												=  depths
				lowerCAmelCase												=  sr_ratios
				lowerCAmelCase												=  hidden_sizes
				lowerCAmelCase												=  patch_sizes
				lowerCAmelCase												=  strides
				lowerCAmelCase												=  mlp_ratios
				lowerCAmelCase												=  num_attention_heads
				lowerCAmelCase												=  hidden_act
				lowerCAmelCase												=  hidden_dropout_prob
				lowerCAmelCase												=  attention_probs_dropout_prob
				lowerCAmelCase												=  classifier_dropout_prob
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  drop_path_rate
				lowerCAmelCase												=  layer_norm_eps
				lowerCAmelCase												=  decoder_hidden_size
				lowerCAmelCase												=  kwargs.get('''reshape_last_stage'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  semantic_loss_ignore_index





class 			lowercase					( lowercase__		):
			lowercase        =     version.parse('''1.11'''		)






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Mapping[str, Mapping[int, str]]:





				"""simple docstring"""


				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]			)






			@property
			def 					UpperCAmelCase							(self  :	str			)       ->	float:





				"""simple docstring"""


				return 1e-4






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	int:





				"""simple docstring"""


				return 12


